More than fifty percent of the hydrocarbon reserves in the world are in carbonate reservoirs. Approximately twenty percent of the reserves in North American are in carbonate formulations..sup.[1] Carbonate reservoirs are expected to dominate world oil production through the next century.
Most petrophysical interpretation techniques have been developed for siliciclastic formations. These procedures, however, are inadequate for predicting the producibility of the carbonate reservoirs, due to the complexity of their pore structure. For example, unlike sandstones, many carbonate sediments have a bimodal or even trimodal pore size distribution..sup.[2] Organisms also play an important role in forming the reservoirs. Interpreting carbonate rocks is further complicated because they undergo significant diagenesis through chemical dissolution, reprecipitation. dolomitization, fracturing, etc. Anhydrite (anhydrous calcium sulfate) sequences are commonly present. The shape and size of the pore network is likely to be heterogeneous, even on a micro length scale. Pore sizes may range from microns to meters, often within a few feet.
Development of a quantitative petrophysical method that covers all features and aspects of carbonates is almost an insurmountable task. All of the aforementioned factors make interpreting these formations difficult.
This invention presents an integrated interpretation methodology for evaluating carbonate reservoirs. Early in the interpretation sequence, the methodology classifies the rock facies. A geometrical model specific to the classification is then used to predict the response of the rock to a variety of stimuli. A reconstruction of the geometrical model is made by comparing the measurements with the model predictions. The model is then used to predict the resistivity and the hydraulic transport properties of the rock, thereby enabling computation of both the reserves and their production behavior.
One of the novel features of the invention is that its geometrical model is dependent on the facies. The facies is deduced from a set of minimally processed logs. The basis for this is that some wellbore measurements are affected by the rock facies. A classification algorithm of the logs may then be used to associate each depth with a given facies. Once this is done, a facies dependent geometrical model is used to process the logs.